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        <!-- Page Contents -->
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            <div class="ui vertical center aligned container">
                <div class="ui text container">
                    <a class="large_links"
                        href="/"><img class="ui centered image" src="{{ STATIC_URL }}img/codalab-logo-onecolor-reverse.png"></a>
                    <h2 class="white-text">an open source platform to learn,
                        create, collaborate through challenges
                    </h2>
                </div>
            </div>
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                <div class="ui middle aligned stackable relaxed grid container">
                    <div class="three column row center aligned">
                        <div class="column">
                            <i class="trophy huge yellow icon"></i>
                            <h3 class="ui header">Participate</h3>
                            <a class="large_links" href="{% url "competitions:list" %}" target="_blank">Codalab competitions</a> |
                            <a class="large_links" href="https://www.codabench.org" target="_blank">Codabench</a>
                        </div>
                        <div class="column">
                            <i class="users huge icon purple"></i>
                            <h3 class="ui header">Organize</h3>
                            <a class="large_links"
                                href="https://github.com/codalab/codalab-competitions/wiki/User_Competition-Roadmap"
                                target="_blank">Competitions</a> |
                            <a class="large_links"
                                href="https://github.com/codalab/competitions-v2/wiki/Getting-started-with-Codabench"
                                target="_blank">Benchmarks</a> |
                            <a class="large_links"
                                href="https://chagrade.lri.fr/" target="_blank">Classes</a>
                        </div>
                        <div class="column">
                            <i class="code huge icon"></i>
                            <h3 class="ui header">Contribute</h3>
                            <a class="large_links"
                                href="https://groups.google.com/g/codalab-competitions"
                                target="_blank"> Google group</a> |
                            <a
                                class="large_links"
                                href="https://github.com/codalab/codalab-competitions"
                                target="_blank">Github</a>
                        </div>
                    </div>

                </div>
            </div>

            <div class="ui six column grid container" id="general-stats">
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                                {{ stat.label }}
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            </div>

            <div class="ui vertical stripe segment">
                <div class="ui middle center aligned stackable grid container">
                    <div class="eight wide left aligned column">
                        <div class="row column-bottom-padding">
                            <h3 class="ui purple header">What are Competitions and
                                Benchmarks?
                            </h3>
                            <p>The <b><a href="https://codalab.lisn.upsaclay.fr/">CodaLab
                                competitions</a></b> platform and its new (beta)
                                version <b><a class="large_links"
                                    href="https://www.codabench.org/">Codabench</a></b>
                                are powerful open source frameworks for running
                                scientific competitions and benchmarks, which involve <b>result</b>
                                or <b>code</b> submission. You can either participate
                                in an existing competition (or benchmark) or host one.
                            </p>
                            <p>Most competitions hosted on such platforms are machine
                                learning (data science) competitions, but they are NOT
                                limited to this application domain. They can accommodate
                                any problem for which a solution can be provided in the
                                form of a zip archive containing a number of files to be
                                evaluated quantitatively by a scoring program (provided
                                by the organizers). The scoring program must return a
                                numeric score, which is displayed on a leaderboard where
                                the performances of participants are compared.<br>
                            </p>
                            <p><b>Codalab</b> is more geared towards organizing <b>limited-time
                                competitive events</b> (challenges) and <b>Codabench</b>
                                is designed for <b>collaborative benchmarking of
                                algorithms against sets of tasks</b> (or vice-versa)
                                over a long period of time. Codabench is more flexible
                                than Codalab and fully backward compatible (old Codalab
                                challenge bundles can be uploaded to Codabench). But is
                                is newer and still in a beta version.<br>
                            </p>
                        </div>
                        <div class="row column-bottom-padding">
                            <h3 class="ui purple header">History of Codalab and
                                Codabench<br>
                            </h3>
                            <p>Codalab was created in 2013 as a joint venture between
                                Microsoft and Stanford University. Originally the vision
                                was to create an ecosystem for conducting computational
                                research in a more efficient, reproducible, and
                                collaborative manner, combining worksheets and
                                competitions. Worksheets capture complex research
                                pipelines in a reproducible way and create "executable
                                papers".<br>
                            </p>
                            <p>In 2014, <b> </b><a class="large_links"
                                href="http://www.chalearn.org/">ChaLearn</a> joined to
                                co-develop Codalab competitions. Since 2015, University
                                Paris-Saclay is community lead of Codalab competitions,
                                under the leadership of <a class="large_links"
                                    href="http://guyon.chalearn.org/">Isabelle Guyon</a>,
                                professor of artificial intelligence. Codalab is
                                administered by <a class="large_links"
                                    href="https://www.lisn.upsaclay.fr/">LISN staff</a>,
                                under the direction of <b><a class="large_links"
                                    href="https://www.linkedin.com/in/anne-catherine-letournel-64a5233/?originalSubdomain=fr">Anne-Catherine
                                Letournel</a></b>.<br>
                            </p>
                            <p>Since 2019 we are developing a new version of Codalab
                                competitions called Codabench, to organize both
                                competitions and benchmarks.
                            </p>
                        </div>
                        <div class="row column-bottom-padding">
                            <h3 class="ui purple header">Research and Education<br>
                            </h3>
                            <p>Codalab is used actively in research. In 2020/2021, 600
                                new challenges were launched. Recent popular challenges
                                organized with Codalab include the <b><a
                                    class="large_links"
                                    href="https://competitions.codalab.org/competitions/26655">AAAI
                                2021 Covid-19 fake news detection</a></b> (over 1000
                                participants), the <a class="large_links"
                                    href="https://competitions.codalab.org/competitions/35575"><b>COVID-19
                                infection percentage estimation</b></a>, the&nbsp; <a
                                    class="large_links"
                                    href="https://competitions.codalab.org/competitions/25276">COVID-19
                                retweet prediction challenge</a>, the <a
                                    class="large_links"
                                    href="https://competitions.codalab.org/competitions/24184">ECCV
                                2020 ChaLearn LAP Fair face recognition challenge</a>,
                                the 2020 <a class="large_links"
                                    href="https://competitions.codalab.org/competitions/21639">DriveML
                                Huawei Autonomous Vehicle Challenge</a>. Recurrent
                                challenges attracting hundreds of participants each year
                                include the <b><a class="large_links"
                                    href="https://semeval.github.io/">SemEval challenge
                                series</a></b> in natural language processing and
                                the <b><a class="large_links"
                                    href="https://chalearnlap.cvc.uab.cat/">ChaLearn LAP
                                challenge series</a></b>&nbsp; in computer vision.
                                High profile challenges include the <a
                                    class="large_links" href="https://web.see4c.eu/">2
                                million Euro prize of the EU, organized by the See.4C
                                consortium</a>, the <a class="large_links"
                                    href="https://competitions.codalab.org/competitions/16652">CIKM
                                AnalytiCup 2017</a>, which attracted 493 participants,
                                <a class="large_links"
                                    href="https://competitions.codalab.org/competitions/3221">MSCOCO</a>
                                (633 participants), the <a class="large_links"
                                    href="https://competitions.codalab.org/competitions/2321">ChaLearn
                                AutoML challenge 2017</a> (687 participants), and the
                                <b><a class="large_links"
                                    href="https://competitions.codalab.org/competitions/17094">Liver
                                Tumor Segmentation Challenge</a></b> (over 4000
                                participants).<br>
                            </p>
                            <p>Since 2016, Codalab offers the possibility of
                                organizing machine learning challenges with code
                                submission. The simplest machine learning challenges
                                require only the submission of results, which are
                                compared to a solution (or key) by a scoring program.
                                Result submission challenges are less computationally
                                expensive than code submission challenges. However, they
                                offer less possibilities. In particular, code submission
                                allows conducting fair benchmarks by executing submitted
                                code in the same condition for all participants.
                            </p>
                            <p>Codalab has been providing free resources for challenge
                                organizers. New since version 1.5: organizers can hook
                                up their own compute workers to the backend of Codalab
                                to redirect the code submissions, enabling growth to big
                                data competitions running at the expense of the
                                organizers. For very special dedicated projects, Codalab
                                can be customized since it is an open source project.<br>
                            </p>
                            <p>
                                Codalab is used in education. For example, a class on <b><a
                                    href="https://codalab.lisn.upsaclay.fr/competitions/226">Artificial
                                Neural Networks and Deep Learning 2021</a> </b>with
                                400 students had homework on Codalab. We implemented a
                                software called <a class="large_links"
                                    href="https://chagrade.lri.fr/"><b>Chagrade</b></a> to
                                help grading homework using Codalab competitions.
                                <meta charset="utf-8">
                            </p>
                        </div>
                    </div>
                    <div class="eight wide center aligned column">
                        <div class="row">
                            <h1 class="ui purple header">News</h1>
                        </div>
                            <div class="row news-column-bottom-padding">
                            <h3><a href="https://codalab.lisn.upsaclay.fr/">New
                                server</a><br>
                            </h3>
                            <p><u>Mars 2022</u>: The <a href="https://codalab.lisn.upsaclay.fr/">new Codalab infrastructure</a> is stable.
                                We have migrated the storage over a distributed Minio
                                (4 physical servers, each with 12 disks of 16 TB)
                                spread over 2 buildings for robustness,
                                and added 10 more GPUs to the existing 10 previous ones in the backend.
                                A lot of horsepower to support Industry-strenght challenge.
                                Thanks for the sponsorship of région Ile-de-France, ANR,
                                Université Paris-Saclay, CNRS, INRIA, and ChaLearn.
                                The new server is faster, runs in Python3, and features a renewed organizer interface.
                            </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                            <h3><a href="https://www.codabench.org/">Codabench</a></h3>
                            <p><u>December 2021</u>: Codabench (beta) is announced at
                                NeurIPS 2021.
                            </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <h3><a href="https://competitions.codalab.org/">Codalab
                                    statistics</a>
                                </h3>
                                <p> <u>August 2020:</u> Codalab exceeds 50,000 users,
                                    1000 competitions (over 400 last year), and ~600
                                    submissions per day!
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="https://l2rpn.chalearn.org/">
                                    <h3>L2RPN</h3>
                                </a>
                                <p> <u>July 2020:</u> We launched a new Learning to Run
                                    a Power Network competition, in collaboration with <a
                                        href="http://www.chalearn.org/">ChaLearn</a> and <a
                                        href="https://www.rte-france.com/">RTE</a>. We have
                                    a <a
                                        href="https://competitions.codalab.org/competitions/25426">robustness</a>
                                    and an <a
                                        href="https://competitions.codalab.org/competitions/25427">adaptability</a>
                                    track. This is an <a
                                        href="https://neurips.cc/Conferences/2020/CompetitionTrack">NeurIPS
                                    2020 competition</a>.
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="https://chagrade.lri.fr/">
                                    <h3>Chagrade</h3>
                                </a>
                                <p> <u>May 2020:</u> We released a new application to
                                    help instructors use challenges in the classroom and
                                    grade them called <a href="https://chagrade.lri.fr/">Chagrade</a>.
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="https://autodl.chalearn.org/">
                                    <h3>AutoDL</h3>
                                </a>
                                <p> <u>April 2020:</u> The <a
                                    href="https://nips.cc/Conferences/2019/CallForCompetitions">NeurIPS</a>
                                    AutoDL challenge ended. But the series of challenges
                                    on Automated Deep Learning, in collaboration with <a
                                        href="http://www.chalearn.org/">ChaLearn</a>, <a
                                        href="https://ai.google/research/join-us/zurich/">Google
                                    Zurich</a>, and <a
                                        href="https://www.4paradigm.com/">4Paradigm</a>
                                    continues with <a
                                        href="https://autodl.lri.fr/competitions/149">AutoSeries</a>
                                    and <a href="https://www.automl.ai/competitions/3">AutoGraph</a>.
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="https://codalab.lri.fr/competitions/522">
                                    <h3>Data Science Africa 2019</h3>
                                </a>
                                <p> <u>June 2019:</u> We organized a data science
                                    bootcamp at <a
                                        href="https://codalab.lri.fr/competitions/522">Data
                                    Science Africa 2019</a> in the form of a challenge
                                    to detect Malaria parasites in microscope images.
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="https://l2rpn.chalearn.org/">
                                    <h3>L2RPN</h3>
                                </a>
                                <p> <u>May 2019:</u> We launched the first Learning to
                                    <a
                                        href="https://competitions.codalab.org/competitions/22845">Run
                                    a Power Network competition</a>, in collaboration
                                    with <a href="http://www.chalearn.org/">ChaLearn</a>
                                    and <a href="https://www.rte-france.com/">RTE</a>.
                                    This is an <a
                                        href="https://www.ijcnn.org/2019-competitions">IJCNN
                                    competition</a>.
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="https://autodl.chalearn.org/">
                                    <h3>AutoDL</h3>
                                </a>
                                <p> <u>October 2018:</u> We are preparing a challenge
                                    on <a
                                        href="https://docs.google.com/a/chalearn.org/viewer?a=v&amp;pid=sites&amp;srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OjJiMTU0MTlmZjY5NGZiOGI">
                                    Automatic Deep Learning (AutoDL) </a> challenging
                                    participants to design code eliminating the need of
                                    human expertise to choose the architecture and
                                    hyper-parameters of deep neural networks. The
                                    challenge is co-organized and sponsored by Google. The
                                    protocol will be tested on Codalab in October.
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="https://hal.inria.fr/hal-01745714/document">
                                    <h3>TrackML</h3>
                                </a>
                                <p> <u>September 2018:</u> The <a
                                    href="https://www.lal.in2p3.fr/">LAL</a> and <a
                                    href="https://home.cern/">CERN</a> are organizing a
                                    challenge to reconstruct particle trajectories in high
                                    energy physics detectors. After the success of the <a
                                        href="https://www.kaggle.com/c/trackml-particle-identification"> first
                                    phase with result submission only</a>, a second
                                    phase with code submission will be run on Codalab.
                                    TrackML is an officially selected challenge of the
                                    NIPS 2018 conference.
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="https://www.4paradigm.com/competition/nips2018">
                                    <h3>AutoML3</h3>
                                </a>
                                <p> <u>August 2018:</u> Codalab is proud to host the
                                    third challenge on Automatic Machine Learning: <a
                                        href="https://competitions.codalab.org/competitions/19836">Lifelong
                                    Machine Learning with drift</a>. AutoML3 is an
                                    officially selected challenge of the NIPS 2018
                                    conference.
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="https://web.see4c.eu/">
                                    <h3>See.4C</h3>
                                </a>
                                <p><u>February 2018:</u> 2 million Euro Big Data EU
                                    prize powered by Codalab.
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="http://dataia.eu/index.php?lang=FR&amp;page=kickoff">
                                    <h3>DataIA</h3>
                                </a>
                                <p><u>February 2018:</u> Isabelle Guyon presents Codalab
                                    at the newly formed Institute of Convergence DataIA
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="https://sites.google.com/a/chalearn.org/saclay/">
                                    <h3>Student Projects</h3>
                                </a>
                                <p><u>January 2018:</u> Paris-Saclay master students
                                    create challenges for L2 students.
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="https://codalab.lri.fr/competitions/134?secret_key=669495b4-175b-47fe-9aa4-ce1b50416f14">
                                    <h3>Homework</h3>
                                </a>
                                <p><u>January 2018:</u> Paris-Saclay instructors create
                                    reinforcement learning homework.
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="https://competitions.codalab.org/">
                                    <h3>10,000 Users</h3>
                                </a>
                                <p><u>December 2017:</u> Codalab exceeds 10000 users
                                    with 480 competitions (145 public)
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="http://ciml.chalearn.org/">
                                    <h3>CiML workshop</h3>
                                </a>
                                <p> <u>December 2017:</u> Codalab presented at the
                                    Challenges in Machine Learning workshop [<a
                                        href="https://docs.google.com/a/chalearn.org/viewer?a=v&amp;pid=sites&amp;srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OjVmY2U0NTk3M2RhYTRlZGY">slides</a>].
                                </p>
                            </div>
                            <div class="row news-column-bottom-padding">
                                <a
                                    href="https://github.com/codalab/codalab-competitions/wiki">
                                    <h3>Version 1.5 is out!</h3>
                                </a>
                                <p> <u>November 2017:</u> Explore the new features:
                                    scale up your code submission competition with your
                                    own compute workers (full privacy, dockers); organize
                                    RL challenges and hook up simulators providing data on
                                    demand (with your own "ingestion program"); use the
                                    ChaLab wizard to create competitions in minutes.
                                </p>
                            </div>

                        <!--<div class="row">
                            <div class="eight wide column">
                                <h3 class="ui purple header">What is Codalab?</h3>
                                <div class="ui divider"></div>
                                <p>CodaLab Competitions is a powerful open source framework for running competitions that involve
                                    result or code submission. You can either participate in an existing competition or host a new
                                    competition. Most competitions hosted on Codalab are machine learning (data science)
                                    competitions, but Codalab is NOT limited to this application domain. It can accommodate any
                                    problem for which a solution can be provided in the form of a zip archive containing a number of
                                    files to be evaluated quantitatively by a scoring program (provided by the organizers). The
                                    scoring program must return a numeric score, which is displayed on a leaderboard where the
                                    performances of participants are compared.</p>
                            </div>
                            <div class="six wide right floated column" style="">
                                <h1 class="ui purple massive bold header">News</h1>
                                <a class="large_links" href="https://web.see4c.eu/"><h3>See.4C</h3></a>
                                <div class="ui divider"></div>
                                <p><u>February 2018:</u> 2 million Euro Big Data EU prize powered by Codalab.</p>
                            </div>
                            </div>
                            <div class="row">
                            <div class="eight wide column">
                                <h3 class="ui purple header">History of Codalab</h3>
                                <div class="ui divider"></div>
                                <p>Codalab was created in 2013 as a joint venture between Microsoft and Stanford University.
                                    Originally the vision was to create an ecosystem for conducting computational research in a more
                                    efficient, reproducible, and collaborative manner, combining worksheets and competitions.
                                    Worksheets capture complex research pipelines in a reproducible way and create "executable
                                    papers". Some competitions have been organized using worksheets, but the competition platform
                                    and the worksheet platform have both a large user base and can be used independently. In 2014,
                                    <a class="large_links" href="http://www.chalearn.org/">ChaLearn</a> joined to co-develop Codalab competitions. Since
                                    2015, University Paris-Saclay is
                                    community lead of Codalab competitions, under the direction of Isabelle Guyon, professor of big
                                    data. Codalab is administered by CKCollab and the <a class="large_links" href="https://www.lri.fr/">LRI staff.</a>
                                </p>
                            </div>
                            <div class="six wide right floated column" style="">
                                <a class="large_links" href="https://sites.google.com/a/chalearn.org/saclay/"><h3>Student Projects</h3></a>
                                <div class="ui divider"></div>
                                <p><u>January 2018:</u> Paris-Saclay master students create challenges for L2 students.</p>
                            </div>
                            </div>
                            <div class="row">
                            <div class="eight wide column">
                                <h3 class="ui purple header">Codalab in Research</h3>
                                <div class="ui divider"></div>
                                <p>Codalab in research
                                    Codalab is used actively in research. In 2016/2017, 88 new challenges were launched. Recent high
                                    profile challenges organized with Codalab include the <a class="large_links" href="https://web.see4c.eu/">2 million
                                        Euro prize of the EU, organized
                                        by the See.4C consortium</a>, the <a class="large_links"
                                        href="https://competitions.codalab.org/competitions/16652">CIKM AnalytiCup 2017</a>,
                                    which attracted 493 participants,
                                    <a class="large_links" href="https://competitions.codalab.org/competitions/3221">MSCOCO</a>
                                    (633 participants) and the <a class="large_links" href="https://competitions.codalab.org/competitions/2321">ChaLearn
                                        AutoML challenge 2017</a> (687 participants).
                                    Since 2016, Codalab offers the possibility of organizing machine learning challenges with code
                                    submission. The simplest machine learning challenges require only the submission of results,
                                    which are compared to a solution (or key) by a scoring program. Result submission challenges are
                                    less computationally expensive than code submission challenges. However, they offer less
                                    possibilities. In particular, code submission allows conducting fair benchmarks by executing
                                    submitted code in the same condition for all participants.
                                    Codalab has been providing free resources for challenge organizers who want to run high impact
                                    events, within a pre-approved agreed upon budget. New since version 1.5: organizers can hook up
                                    their own compute workers to the backend of Codalab to redirect the code submissions, enabling
                                    growth to big data competitions running at the expense of the organizers. For very special
                                    dedicated projects, Codalab can be customized since it is an open source project.</p>
                            </div>
                            <div class="six wide right floated column" style="">
                                <div style="padding-bottom: 50px;">>
                                    <a class="large_links" href="https://codalab.lri.fr/competitions/134?secret_key=669495b4-175b-47fe-9aa4-ce1b50416f14">
                                        <h3>Homework</h3></a>
                                    <div class="ui divider"></div>
                                    <p><u>January 2018:</u> Paris-Saclay instructors create reinforcement learning homework.</p>
                                </div>
                                <div style="padding-bottom: 50px;">>
                                    <a class="large_links" href="https://competitions.codalab.org/"><h3>Homework</h3></a>
                                    <div class="ui divider"></div>
                                    <p><u>December 2017:</u> Codalab exceeds 10000 users with 480 competitions (145 public)</p>
                                </div>
                            </div>
                            </div>
                            <div class="row">
                            <div class="eight wide column">
                                <h3 class="ui purple header">Codalab in Teaching</h3>
                                <div class="ui divider"></div>
                                <p>Since 2015, Codalab is used in teaching at University Paris-Saclay. Master student create
                                    <a class="large_links" href="https://sites.google.com/a/chalearn.org/saclay/">challenges as a project</a> (in only
                                    seven sessions of two hours). The challenges are then solved by
                                    undergraduate students. Learning how to organize a challenge teaches the rigor of formulating
                                    well a problem, with good data, enough data, good metrics, and a well defined question to be
                                    answered. We developed a step-by-step wizard (ChaLab) to facilitate entering the challenge on
                                    the platform, so the students can focus more on the scientific aspects. You can borrow the
                                    student instructions if you want to use this in your class as well (<a class="large_links"
                                        href="https://www.dropbox.com/s/o34mfzanszwwzoq/Instructions_Projet_M2Info.pdf?dl=0">full
                                        syllabus for 2018</a>;
                                    <a class="large_links" href="https://www.dropbox.com/s/kr8um9djawdxo71/Starting_Kit_M2info.pdf?dl=0">instructions to
                                        prepare a challenge</a>). We grade the master students using various aspects of
                                    their work: proposal, website, testing, report, video, presentation in class. The undergraduate
                                    students solve the challenges in 12 weeks and are also graded similarly for their proposal,
                                    submissions to the challenge, report, video, and presentations in class. More recently, Codalab
                                    is also being used to <a class="large_links" href="https://codalab.lri.fr/competitions/134?secret_key=669495b4-175b-47fe-9aa4-ce1b50416f14">grade
                                        homework of a reinforcement learning class.</a></p>
                            </div>
                            <div class="six wide right floated column" style="">
                                <div style="padding-bottom: 50px;">
                                    <a href="http://ciml.chalearn.org/"><h3>CiML workshop</h3></a>
                                    <div class="ui divider"></div>
                                    <p>
                                        <u>December 2017:</u> Codalab presented at the Challenges in Machine Learning workshop [<a href="https://docs.google.com/a/chalearn.org/viewer?a=v&pid=sites&srcid=Y2hhbGVhcm4ub3JnfHdvcmtzaG9wfGd4OjVmY2U0NTk3M2RhYTRlZGY">slides</a>].
                                    </p>
                                </div>
                                <div style="padding-bottom: 50px;">
                                    <a href="https://github.com/codalab/codalab-competitions/wiki"><h3>Version 1.5 is out!</h3></a>
                                    <div class="ui divider"></div>
                                    <p>
                                        <u>November 2017:</u> Explore the new features: scale up your code submission competition with your own compute workers (full privacy, dockers); organize RL challenges and hook up simulators providing data on demand (with your own "ingestion program"); use the ChaLab wizard to create competitions in minutes.
                                    </p>
                                </div>
                            </div>
                            </div>-->
                        </div>
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